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. Advance Bayesian and ensemble learning approaches for non-stationary temporal processes. Implement probabilistic diffusion or generative models for long-term forecasting. Collaborate closely with
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uncertainty from climate projections into land-use forecasts. Advance Bayesian and ensemble learning approaches for non-stationary temporal processes. Implement probabilistic diffusion or generative models
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has a strong background in control engineering, with documented expertise in optimal control, adaptive control and online optimization, stochastic systems, Bayesian inference, and state estimation and
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MOFs and their hybrid materials. The primary objective is to explore the fundamental structure–property relationships that govern MOF vitrification and liquid-phase behavior, and to develop new MOF-based
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-invasive battery lifetime prediction through acoustic emission detection. This involves modeling the battery as a 3D acoustic landscape and tracking its changes over time. The expected outcomes
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. Application must be sent via the electronic recruitment system, using the link below. Application must include: Cover letter (summary of research skills) discussing your alignment with the research objectives
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integrates innovative oceanographic, biogeochemical, ecophysiological, and model-based investigations. The main research objective is the exploration and quantification of non-photosynthetic sources of oxygen
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while maintaining a clear focus on objectives. Commitment to foster collaboration and innovation with local public institutions and other stakeholders (e.g. PPR, schools, youth clubs, public
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tasks You will join the Grand Solution project 'DRONES: Drone-Obtained Electromagnetic Signatures' to exploore innovative approaches such as drone-based radio testing for large objects and EM signiture
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completion of) a PhD in Computer Science or Mathematics. An ideal candidate has a research track record at a high international level (reflecting the academic age of the applicant) and experience with